Abstract:

           This paper evaluates face recognition applied to the real-world application of Facebook. Because papers usually present results in terms of accuracy on constrained face datasets, it is difficult to assess how they would work on natural data in a real-world application. We present a method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces representing over 500 users. From these natural face datasets, we evaluate a variety of well-known face recognition algorithms (PCA, LDA, ICA, SVMs) against holistic performance metrics of accuracy, speed, memory usage, and storage size. SVMs perform best with ~65% accuracy, but lower accuracy algorithms such as IPCA are orders of magnitude more efficient in memory consumption and speed, yielding a more feasible system.

Facebook Picture and Tag Downloader	Matlab Face Recognition Evaluator	FG 2008 Powerpoint
Face Extractor

Publication

B. C. Becker, E. G. Ortiz. "Evaluation of Face Recognition Techniques for Application to Facebook". IEEE International Conference on Automatic Face and Gesture Recognition. [pdf]